Transfer of the whole microbiota may not reduce diabetes incidence despite a major change in gut microbiota, but single symbionts such as with beneficial metabolic and immune signalling effects may reduce diabetes incidence when administered as a probiotic.

Faecalibacterium prausnitzii is considered as one of the most important bacterial indicators 32 of a healthy gut. We studied the effects of oral F. prausnitzii treatment on high-fat fed mice. 33Compared to the High-fat Control mice, F. prausnitzii-treated mice had lower hepatic fat 34 content, AST and ALT, and increased fatty-acid oxidation and adiponectin signaling in liver.

Metagenomic approaches focus on taxonomy or gene annotation but lack power in defining functionality of gut microbiota. Therefore, metaproteomics approaches have been introduced to overcome this limitation. However, the common metaproteomics approach uses data-dependent acquisition mass spectrometry, which is known to have limited reproducibility when analyzing samples with complex microbial composition. In this work, we provide a proof of concept for data-independent acquisition (DIA) metaproteomics. To this end, we analyze metaproteomes using DIA mass spectrometry and introduce an open-source data analysis software package, diatools, which enables accurate and consistent quantification of DIA metaproteomics data. We demonstrate the feasibility of our approach in gut microbiota metaproteomics using laboratoryassembled microbial mixtures as well as human fecal samples.

FFs condition microbiota, affect colon homeostasis and are important components of natural, diabetes-promoting diets in NOD mice.

No abstract